基于BIRCH聚类的L-Transformer分布式光伏短期发电功率预测  

Short-term Distributed Photovoltaic Power Generation Prediction Based on BIRCH Cluster-ing and L-Transformer

作　　者：董俊[1,2] 刘瑞 束洪春 罗琨[1,2] 刘壮 DONG Jun;LIU Rui;SHU Hongchun;LUO Kun;LIU Zhuang(School of Electric Power Engineering,Kunming University of Science and Technology,Kunming 650504,China;Yunnan Key Laboratory of Green Energy and Digital Power Measurement Control and Protection,Kunming 650504,China)

机构地区：[1]昆明理工大学电力工程学院,昆明650504 [2]云南省绿色能源与数字电力量测及控保重点实验室,昆明650504

出　　处：《高电压技术》2024年第9期3883-3893,I0006-I0008,共14页High Voltage Engineering

基　　金：云南省重大科技专项计划(202002AF080001)。

摘　　要：精准的分布式光伏短期发电功率预测有助于电力系统运行与功率就地平衡。该文提出一种基于BIRCH(balanced iterative reducing and clustering using hierarchies)相似日聚类的L-Transformer(LSTM-Transformer)模型进行短期光伏功率预测。首先使用BIRCH无监督聚类算法对历史数据聚类得到3种典型天气,根据聚类结果划分测试集对模型进行训练。为提高不同天气类型下的预测精度,采用双层架构的L-Transformer模型,首层通过长短期记忆网络(long short term memory,LSTM)的门控单元机制捕捉时间序列中的长期依赖关系;次层结合Transformer模型的自注意力机制聚焦于当前任务更关键的特征量,通过多注意力头与光伏数据特征量相结合生成向量,注意力头并行计算,从而高效、精确地预测短期光伏功率。实测数据验证结果表明L-Transformer模型对于不同天气类型功率预测泛化性优异、精确度高,气象数据波动大时鲁棒性强。Accurate short-term power prediction of distributed photovoltaic systems is helpful to the operation of power systems and local power balancing.This paper proposes an LSTM-Transformer(L-Transformer)model based on BIRCH(balanced iterative reducing and clustering using hierarchies)for short-term photovoltaic power forecasting.First,the BIRCH unsupervised clustering algorithm is used to cluster historical data into three typical weather patterns,and the test set is divided according to the clustering results to train the model.To improve the prediction accuracy under different weather types,a two-layer L-Transformer model is adopted.The first layer captures long-term dependencies in the time series by using the gating unit mechanism of long short term memory(LSTM).The second layer combines the self-attention mechanism of the transformer model,focusing on the more critical features of the current task.By integrat-ing multiple attention heads with photovoltaic data feature quantities to generate vectors,the parallel computation of attention heads will efficiently and accurately predict short-term photovoltaic power.The results,combined with actual measurement data,show that the L-Transformer model has excellent generalization and high accuracy for power predic-tion under different weather types,and the robustness is strong when meteorological data fluctuations are remarkable.

关 键 词：深度学习 自注意力机制 多头注意力 BIRCH聚类 短期光伏功率预测 特征融合 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM615[自动化与计算机技术—控制科学与工程]